Abstract: A volumetric segmentation method is disclosed for brain region analysis, in particular but not limited to, regions of the basal ganglia such as the subthalamic nucleus (STN). This serves for visualization and localization within the sub-cortical region of the basal ganglia, as an example of prediction of a region of interest for deep brain stimulation procedures. A statistical shape model is applied for variation modes of the STN, or the corresponding regions of interest, and its predictors on high-quality training sets obtained from high-field, e.g., 7T, MR imaging. The partial least squares regression (PLSR) method is applied to induce the spatial relationship between the region to be predicted, e.g., STN, and its predictors. The prediction accuracy for validating the invention is evaluated by measuring the shape similarity and the errors in position, size, and orientation between manually segmented STN and its predicted one.

Abstract: The present disclosure describes methods and systems for risk detection and intervention for neurodevelopmental disorders. The method includes assessment of risk level, guidance and treatment recommendations and strategies, and longitudinal monitoring of patients with neurodevelopmental disorders. The assessments and monitoring can be integrated into the patient's health care program and electronic health record (EHR).

Abstract: A system and method are provided for creating magnetic resonance (MR) images with reduced motion artifacts from the MR data from which the images are produced. The method includes selecting a candidate image from a plurality of candidate images reconstructed from the MR data. The method also includes registering the candidate image to a reference image, comparing the candidate image to a consistency map, and, based on comparing the candidate image using the consistency map, selecting a blending algorithm. The method also includes generating a blended image using the blending algorithm and the candidate image and repeating these steps for each candidate image. The method also includes performing a Fourier aggregation to generate a combined image and displaying the combined image with reduced motion artifacts compared to the plurality of candidate images.

Abstract: A method for automated detection of cervical pre-cancer includes: providing at least one cervigram; pre-processing the at least one cervigram; extracting features from the at least one pre-processed cervigram; and classifying the at least one cervigram as negative or positive for cervical pre-cancer based on the extracted features.

Abstract: A volumetric segmentation method is disclosed for brain region analysis, in particular but not limited to, regions of the basal ganglia such as the subthalamic nucleus (STN). This serves for visualization and localization within the sub-cortical region of the basal ganglia, as an example of prediction of a region of interest for deep brain stimulation procedures. A statistical shape model is applied for variation modes of the STN, or the corresponding regions of interest, and its predictors on high-quality training sets obtained from high-field, e.g., 7T, MR imaging. The partial least squares regression (PLSR) method is applied to induce the spatial relationship between the region to be predicted, e.g., STN, and its predictors. The prediction accuracy for validating the invention is evaluated by measuring the shape similarity and the errors in position, size, and orientation between manually segmented STN and its predicted one.

Abstract: Disclosed embodiments include methods and computer systems for brain image prediction or segmentation. A clinical image file of data representative of a patients' brain image, including structures of interest (SOI) such as the subthalamic nucleus (STN), is applied to and processed by a segmentation process. The segmentation process uses one or more machine learning approaches such as trained deep learning models to identify the SOI in the clinical image. Output by the segmentation process is a segmented image file of data representing the brain image in which the structures of interest (SOI) are segmented. By the segmentation process, the SOI in clinical image, including the locations, orientations and/or boundaries of the SOI, are accurately predicted or identified, and can thereby be presented in an enhanced visualization form (e.g., highlighted) in the segmented image.

Abstract: The subject matter described herein includes methods, systems, and computer readable media for conducting an automatic assessment of postural control of a subject. According to one aspect, a method occurs at a computing platform including a processor and memory. The method includes displaying a stimulus to which a subject responds, capturing facial image data of the subject, analyzing the facial image data to determine a frequency of head displacement information associated with the subject, using the head displacement information to derive postural control assessment data, and determining that the postural control assessment data is indicative of a neurodevelopmental or neuropsychiatric disorder associated with the subject.

Abstract: A volumetric segmentation method is disclosed for brain region analysis, in particular but not limited to, regions of the basal ganglia such as the subthalamic nucleus (STN). This serves for visualization and localization within the sub-cortical region of the basal ganglia, as an example of prediction of a region of interest for deep brain stimulation procedures. A statistical shape model is applied for variation modes of the STN, or the corresponding regions of interest, and its predictors on high-quality training sets obtained from high-field, e.g., 7T, MR imaging. The partial least squares regression (PLSR) method is applied to induce the spatial relationship between the region to be predicted, e.g., STN, and its predictors. The prediction accuracy for validating the invention is evaluated by measuring the shape similarity and the errors in position, size, and orientation between manually segmented STN and its predicted one.

Abstract: A volumetric segmentation method is disclosed for brain region analysis, in particular but not limited to, regions of the basal ganglia such as the subthalamic nucleus (STN). This serves for visualization and localization within the sub-cortical region of the basal ganglia, as an example of prediction of a region of interest for deep brain stimulation procedures. A statistical shape model is applied for variation modes of the STN, or the corresponding regions of interest, and its predictors on high-quality training sets obtained from high-field, e.g., 7 T, MR imaging. The partial least squares regression (PLSR) method is applied to induce the spatial relationship between the region to be predicted, e.g., STN, and its predictors. The prediction accuracy for validating the invention is evaluated by measuring the shape similarity and the errors in position, size, and orientation between manually segmented STN and its predicted one.

Abstract: Systems and methods for designing, optimizing, patterning, forming, and manufacturing symphotic structures are described herein. A symphotic structure may be formed by identifying a continuous refractive index distribution calculated to convert each of a plurality of input reference waves to a corresponding plurality of output object waves. The continuous refractive index distribution can be modeled as a plurality of subwavelength voxels. The system can calculate a symphotic pattern as a three-dimensional array of discrete dipole values to functionally approximate the subwavelength voxels. A symphotic structure may be formed with a volumetric distribution of dipole structures. A dipole value, such as a dipole moment (direction and magnitude) of each dipole is selected for the volumetric distribution to convert a plurality of input reference waves to a target plurality of output object waves.

Abstract: A system and method are provided for creating magnetic resonance (MR) images with reduced motion artifacts from the MR data from which the images are produced. The method includes selecting a candidate image from a plurality of candidate images reconstructed from the MR data. The method also includes registering the candidate image to a reference image, comparing the candidate image to a consistency map, and, based on comparing the candidate image using the consistency map, selecting a blending algorithm. The method also includes generating a blended image using the blending algorithm and the candidate image and repeating these steps for each candidate image. The method also includes performing a Fourier aggregation to generate a combined image and displaying the combined image with reduced motion artifacts compared to the plurality of candidate images.

Abstract: The subject matter described herein includes methods, systems, and computer readable media for automated attention assessment. According to one method, a method for automated attention assessment includes obtaining head and iris positions of a user using a camera while the user watches a display screen displaying a video containing dynamic region-based stimuli designed for identifying a neurodevelopmental and/or psychiatric (neurodevelopmental/psychiatric) disorder; analyzing the head and iris positions of the user to detect attention assessment information associated with the user, wherein the attention assessment information indicates how often and/or how long the user attended to one or more regions of the display screen while watching the video; determining that the attention assessment information is indicative of the neurodevelopmental/psychiatric disorder; and providing, via a communications interface, the attention assessment information, a diagnosis, or related data.

Abstract: A method for automated detection of cervical pre-cancer includes: providing at least one cervigram; pre-processing the at least one cervigram; extracting features from the at least one pre-processed cervigram; and classifying the at least one cervigram as negative or positive for cervical pre-cancer based on the extracted features.

Abstract: The subject matter described herein includes methods, systems, and computer readable media for automated behavioral assessment. According to one aspect, a method for automated behavioral assessment is provided. The method occurs at a computing platform including a processor and memory. The method includes providing at least one stimulus for eliciting a response from a user. The method also includes obtaining, using a camera or sensor communicatively coupled to the computing platform, the at least one response. The method also includes determining, using the at least one response, a behavioral assessment associated with the user.

Abstract: A volumetric segmentation method is disclosed for brain region analysis, in particular but not limited to, regions of the basal ganglia such as the subthalamic nucleus (STN). This serves for visualization and localization within the sub-cortical region of the basal ganglia, as an example of prediction of a region of interest for deep brain stimulation procedures. A statistical shape model is applied for variation modes of the STN, or the corresponding regions of interest, and its predictors on high-quality training sets obtained from high-field, e.g., 7T, MR imaging. The partial least squares regression (PLSR) method is applied to induce the spatial relationship between the region to be predicted, e.g., STN, and its predictors. The prediction accuracy for validating the invention is evaluated by measuring the shape similarity and the errors in position, size, and orientation between manually segmented STN and its predicted one.

Abstract: A volumetric segmentation method is disclosed for brain region analysis, in particular but not limited to, regions of the basal ganglia such as the subthalamic nucleus (STN). This serves for visualization and localization within the sub-cortical region of the basal ganglia, as an example of prediction of a region of interest for deep brain stimulation procedures. A statistical shape model is applied for variation modes of the STN, or the corresponding regions of interest, and its predictors on high-quality training sets obtained from high-field, e.g., 7T, MR imaging. The partial least squares regression (PLSR) method is applied to induce the spatial relationship between the region to be predicted, e.g., STN, and its predictors. The prediction accuracy for validating the invention is evaluated by measuring the shape similarity and the errors in position, size, and orientation between manually segmented STN and its predicted one.

Abstract: Systems and methods for designing, optimizing, patterning, forming, and manufacturing symphotic structures are described herein. A symphotic structure may be formed by identifying a continuous refractive index distribution calculated to convert each of a plurality of input reference waves to a corresponding plurality of output object waves. The continuous refractive index distribution can be modeled as a plurality of subwavelength voxels. The system can calculate a symphotic pattern as a three-dimensional array of discrete dipole values to functionally approximate the subwavelength voxels. A symphotic structure may be formed with a volumetric distribution of dipole structures. A dipole value, such as a dipole moment (direction and magnitude) of each dipole is selected for the volumetric distribution to convert a plurality of input reference waves to a target plurality of output object waves.

Abstract: Methods and systems for large-scale face recognition. The system includes an electronic processor to receive at least one image of a subject of interest and apply at least one subspace model as a splitting binary decision function on the at least one image of the subject of interest. The electronic processor is further configured to generate at least one binary code from the at least one splitting binary decision function. The electronic processor is further configured to apply a code aggregation model to combine the at least one binary codes generated by the at least one subspace model. The electronic processor is further configured to generate an aggregated binary code from the code aggregation model and use the aggregated binary code to provide a hashing scheme.

Abstract: System and Method for automatically removing blur and noise in a plurality of digital images. The system comprises an electronic processor configured to receive the plurality of digital images, perform motion estimation and motion compensation to align the plurality of digital images, determine an alignment of the plurality of digital images with respect to a reference frame, generate a consistency map based on the alignment of the plurality of digital images with respect to the reference frame, combine the plurality of digital images aligned with respect to the reference frame in the Fourier domain using a quality of alignment information from the consistency map to generate an aggregated frame, and apply a post-processing filter to enhance the quality of the aggregated frame.

Abstract: A volumetric segmentation method is disclosed for brain region analysis, in particular but not limited to, regions of the basal ganglia such as the subthalamic nucleus (STN). This serves for visualization and localization within the sub-cortical region of the basal ganglia, as an example of prediction of a region of interest for deep brain stimulation procedures. A statistical shape model is applied for variation modes of the STN, or the corresponding regions of interest, and its predictors on high-quality training sets obtained from high-field, e.g., 7T, MR imaging. The partial least squares regression (PLSR) method is applied to induce the spatial relationship between the region to be predicted, e.g., STN, and its predictors. The prediction accuracy for validating the invention is evaluated by measuring the shape similarity and the errors in position, size, and orientation between manually segmented STN and its predicted one.